I made an open-source video editor

Hacker News

It's an open source video editor designed for motion effects and versatility. developed with electronjs / TypeScript, FFmpeg, Litjs. I'm currently focusing on making it easier to edit long-form videos. Specifically, adding subtitles to podcast episodes or creating short-form content from YouTube videos. And I’m also developing several extensible utilities. For example, recording video with a floating webcam—similar to Loom—or using LLMs to make cut editing easier. To make all this possible, I believe the first step is to build a versatile, general-purpose video editor. Demo : https://www.youtube.com/watch?v=Bh06VOYSMIM

Visit product View on Hacker NewsJan 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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